Matthias Stephan Bauer
now a Senior Research Scientist at DeepMind
Publications
Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Stephan Bauer, Mark van der Wilk, Carl Edward Rasmussen, 2016. (In Advances in Neural Information Processing Systems 29).
Abstract▼ URL
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.
Comment: arXiv
Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard Turner, April 2019. (In 7th International Conference on Learning Representations). New Orleans.
Abstract▼ URL
This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce , an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. Versa substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate on benchmark datasets where the method sets new state-of-the-art results, and can handle arbitrary number of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.